Skip to main content
SpringerLink
Log in

A new chain-processing-based computer vision system for automatic checking of machining set-up application for machine tools safety

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In high-speed machining, it is of key importance to avoid any collision between the machine tool and the machining set-up. If the machining set-up has not been assembled correctly by the operator and does not conform to the 3D CAD model used to compute the 3D trajectory sent to the machining unit, such collisions may occur. This paper presents a new chain-processing-based computer vision system to automatically avoid collision between tool and machining set-up components by checking that the actual machining set-up is in conformity with the desired 3D CAD model used to generate the tool trajectory. This computer vision system utilizes a single camera to automatically check conformity before the start of the machining operation. The proposed solution was tested in different kinds of machining set-ups, and each step of the proposed chain was evaluated. The results show the robustness of the solution for different kinds of machining set-ups.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad R, Tichadou S, Hascoët J-Y Integration of vision based image processing for multi-axis CNC machine tool safe and efficient trajectory generation. International Journal of Machine Engineering 10(4)

  2. Zhang X, Tsang W-M, Kazuo Y, Masahiko M (2013) A study on automatic on-machine inspection system for 3D modeling and measurement of cutting tools. J Intell Manuf 24(1):71–86

  3. Zhang X, Tian X, Kazuo Y (2009) On-machine 3D vision system for machining setup modeling. Int J Adv Manuf Technol 48(1–4):251–265

    Google Scholar 

  4. Zhang X, Tian X, Kazuo Y, Makoto F (2007) 3D reconstruction and pose determination of the cutting tool from a single view. In: ISVC (2)’07, pp 377–386

  5. Rafael Grompone VG, Jeremie J, Jean-Michel M, Gregory R (2010) LSD: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell 32:722–732

    Article  Google Scholar 

  6. Burns J, Hanson A, Riseman E (1986) Extracting straight lines. IEEE Trans Pattern Anal Mach Intell 8(4):425–455

    Article  Google Scholar 

  7. Deschenes F, Ziou D, Auclair-Fortier M-F (2004) Detection of lines, line junctions and line terminations. Int J Remote Sens 25(3):511–535

    Article  Google Scholar 

  8. Grompone von Gioi R, Jakubowicz J, Morel J-M, Randall G (2010) LSD: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell 32(4):722–732

    Article  Google Scholar 

  9. Papari G, Petkov N, Regli WC (2011) Edge and line oriented contour detection: state of the art. Image Vis Comput 29:79–103

    Article  Google Scholar 

  10. Hartley RI, Zisserman A Multiple view geometry in computer vision

  11. Karabagli B, Simon T, Orteu J-J (2011) Détermination automatique du seuil de binarisation des modules des gradients par modélisation de leur histogramme. In: Proceedings of ORASIS 2011, Praz-sur-Arlym, France, pp 2–8

  12. Beveridge J, Graves C, Lesher C Some lessons learned from coding the burns line extraction algorithm in the Darpa image understanding environment. Technical Report CS-96-125, Computer Science Dept. Colorado State Univ.

  13. Duchenne O, Bach F, Kweon I-S, Ponce J (2011) A tensor-based algorithm for high-order graph matching. IEEE Trans Pattern Anal Mach Intell 33:2383–2395

    Article  Google Scholar 

  14. Gori M, Maggini M, Sarti L (2005) Exact and approximate graph matching using random walks. IEEE Trans Pattern Anal Mach Intell 27(7):1100–1111

    Article  Google Scholar 

  15. Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intell 15(11):1101–1113

    Article  Google Scholar 

  16. Leordeanu M, Hebert M (2005) A spectral technique for correspondence problems using pairwise constraints. In: International Conference of Computer Vision (ICCV), vol 2, pp 1482–1489

  17. Grimson W, Lozano-Pérez T (1987) Localizing overlapping parts by searching the interpretation tree. IEEE Trans Pattern Anal Mach Intell 9(4):469–482

    Article  Google Scholar 

  18. Gabriel RK, Sokal RR (1969) A new statistical approach to geographic variation analysis. Syst Zool 18 (3):259–278

    Article  Google Scholar 

  19. Zhang J, Berg A, Maire M, Malik J SVM-KNN discriminative nearest neighbor classification for visual category recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

  20. Delaunay B (1934) Sur la sphère vide. A la mémoire de Georges Voronoï. Bulletin de l’Académie des Sciences de l’URSS 6:793–800

    Google Scholar 

  21. Liotta G (1996) Low degree algorithms for computing and checking gabriel graphs

  22. Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

  23. Zass R, Shashua A (2008) Probabilistic graph and hypergraph matching, pp 1–8

  24. Bouguet J-Y (2004) Camera calibration toolbox for matlab

  25. Haralick R, Lee C, Ottenberg K, Nôlle M (1991) Analysis and solutions of the three point perspective pose estimation problem, pp 592–598

  26. Horaud R, Monga O (1993) Vision par ordinateur, outils fondamentaux

  27. Shi J, Jitendra M (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  28. Nacereddine N, Tabbone S, Ziou D, Hamami L (2009) L’algorithme EM et le modèle de mélanges de gaussiennes généralisées pour la segmentation d’images. Application au contrôle des joints soudés par radiographie, Conf. Traitement et Analyse de l’Information : Méthodes et Applications. pp 217–222

  29. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9 (1):62–66

    Article  MathSciNet  Google Scholar 

  30. Arcelli C, Lam K-M, Siu W-C (1985) Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recogn 36:721–729

    Google Scholar 

  31. Bai X, Latecki L, Liu W-Y (2007) Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans Pattern Anal Mach Intell 29(3):449–462

    Article  Google Scholar 

  32. Bouguila N, Ziou D, Hammoud RI- (2007) A Bayesian non-gaussian mixture analysis: application to eye modeling. Conf Comput Vis Pattern Recognit, 2007:1–8

  33. Petrovic N, Jovanov L, Pizurica A, Wilfried P (2008) Object tracking using naive Bayesian classifiers 5259:775–784

  34. Terrades O, Valveny E, Tabbone S (2009) Optimal classifier fusion in a non-Bayesian probabilistic framework. Conf Comput Vis Pattern Recognit, 2007 31(9):1630–1644

    Google Scholar 

  35. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  36. Karabagli B, Auclair-Fortier M-F, Simon T, Orteu J-J (2013) Depth map estimation from a single RGB image: principle and calibration, Séjour à l’Université de Sherbrooke, Québec, présenté au congrès de l’Ecole Doctorale EDSYS, Tarbes

  37. Daniel P Peut-on extraire le relief d’une seule image ?, Ph.D. thesis, Institut de Recherche en Informatique de Toulouse (28 Janvier 2000)

Download references

Author information

Authors and Affiliations

  1. Mines Albi, ICA (Institut Clément Ader), Université de Toulouse, Campus Jarlard, F-81013, Albi, France

    Bilal Karabagli, Thierry Simon & Jean-José Orteu

  2. IUT de Figeac, Université de Toulouse, Avenue de Nayrac, 46100, Figeac, France

    Bilal Karabagli & Thierry Simon

Authors
  1. Bilal Karabagli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thierry Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-José Orteu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-José Orteu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karabagli, B., Simon, T. & Orteu, JJ. A new chain-processing-based computer vision system for automatic checking of machining set-up application for machine tools safety. Int J Adv Manuf Technol 82, 1547–1568 (2016). https://doi.org/10.1007/s00170-015-7438-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7438-y

Keywords

Search

Navigation